Sklearn_Linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib   import pyplot as plt\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nadun\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "boston=load_boston()\n",
    "X=boston.data\n",
    "y=boston.target"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13) [[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00]\n",
      " [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00]\n",
      " [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]\n",
      " ...\n",
      " [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00]\n",
      " [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00]\n",
      " [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]\n",
      "(506,) [24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
      " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
      " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
      " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
      " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
      " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
      " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
      " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
      " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
      " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
      " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
      " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
      " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
      " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
      " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
      " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
      " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
      " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
      " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
      " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
      " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
      " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
      " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
      " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
      " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
      " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
      " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
      " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
      "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
      " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
      " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
      " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
      " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
      " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
      " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
      "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
      " 22.  11.9]\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X)\n",
    "print(y.shape,y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "linear_reg=linear_model.LinearRegression()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X.T[5],y)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,y,train_size=0.33)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "model=linear_reg.fit(x_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31.41232961 19.17419591 24.64665328 25.81755921 21.61128027 17.78394854\n",
      " 28.25036946 31.07651676 13.27426021 19.26805585 16.83075948 24.54796845\n",
      " 27.34682471 20.63791317 24.40316121  5.28899453 24.47882232 27.24398559\n",
      " 31.44965909 18.90322762 18.06750201 20.95009321 13.5558493  26.22276618\n",
      " 22.32531427 24.74112966 20.16623369 24.82243284  5.67093161 33.59026311\n",
      " 16.5652366  26.14127848 43.9505475  28.82639671 25.92224562 20.99460246\n",
      " 18.9404361  19.55141148 24.24289109  7.15528915 12.40938936 31.9424957\n",
      " 17.16785384 27.87829068 30.42263206 19.39050411 10.80837648 22.61650674\n",
      " 33.3018631  15.19253919 17.91412949 28.66944829  4.23227432 25.32242513\n",
      " 33.77827675 18.58993517 19.61932858 26.14744142 28.05878054 37.77267289\n",
      " 24.82870949 34.81085583 31.48692466 17.06474788 10.95347434 32.48542031\n",
      " 16.52076785 14.8210867  10.48544502 22.27603938 13.8092519  17.99755023\n",
      "  9.75513839  4.17604726 18.82101899 20.35566154 22.83293968 16.95209787\n",
      " 26.51844053 19.26433965 20.5093989  14.33675387 16.98855662 23.04236386\n",
      " 37.17042273 21.3548002   9.91736801 21.50482193 27.45315146 18.19686024\n",
      " 10.23621458 15.18801341 19.14671856 21.2865902  21.81140032 10.51603206\n",
      " 26.0610239  16.30194835 19.54626958 20.54020714  7.73781273 14.20744441\n",
      " 26.59092695 24.56398504 17.33123993 15.63575586 25.92977437 12.61261035\n",
      " 20.22645298 15.63802826 19.69313509 41.56454352 14.67383555 10.06973535\n",
      " 32.1554908  14.31233156 29.19809651 33.08419314 14.86903639 29.74050369\n",
      "  3.69242081 18.87521182 20.9621577  14.92522473 18.42223906  4.51885282\n",
      " 26.40218187 25.6868299  29.26596646 38.16509486 -6.12406731 40.26217401\n",
      " 11.25820129 14.37189178 16.15046205 22.18704776 17.23129422 16.24035676\n",
      " 41.52045164 19.6514555  14.43303779  3.71178046 24.74124527 29.06844348\n",
      " 23.94234253  9.20440522 12.24166829 28.30967585 33.31434252 22.95443497\n",
      " 39.60613528 25.8641814  22.68761877 21.96268863 30.16298164 20.77296044\n",
      " 29.77152697 22.81323431 20.80076795 18.47263914 17.93511797 15.01627608\n",
      " 12.95111648 25.34279952 33.64283834 19.45277042 23.8919874  23.78586588\n",
      " 38.7137514  13.43396521 18.28687875 28.35789289 24.09222217 14.12597885\n",
      " 27.06721529 25.97536247 20.70597601 13.41522376 20.4772776  42.54820997\n",
      " 16.57693412 21.05973624 31.88127282 34.20787857 26.15064044 13.36446681\n",
      " 22.79352072  3.6084654  19.26354723 28.72949575 27.43583607 15.38451692\n",
      " 16.63709081 25.72117141 36.04886001 36.09500207 14.52808284 17.66928146\n",
      " 23.36149558 24.7082299  17.56561051  9.34017967 39.01213386 17.18893386\n",
      " 19.58995766 31.85082028 27.73232287 32.29859236 22.9099336  19.3450014\n",
      " 35.6803307  16.41311749 26.92078979 20.4480862  17.52460742 32.80133606\n",
      " 17.82291333 26.80297364 19.62849264 20.02642375 26.56360427 16.07657702\n",
      " 22.88631787 21.94570029 22.8209836  11.04672097 25.22513913 17.69747841\n",
      " 16.29085678 12.71173563 20.76472981 18.19696997 21.18340955 27.61117786\n",
      " 29.39188658 11.67722619 18.42702919 30.52782289 34.97000829 28.99301616\n",
      " 19.51350146 31.29619819 22.56700954 22.96410614 14.69594007 30.22311238\n",
      " -2.57939139 21.90299858 24.93272554 35.69305878 17.43535481 35.62799337\n",
      " 34.79794887 24.01932673 32.95598385 35.17504948 28.32155565 20.91249825\n",
      "  7.48246394 32.80449754 20.93836684 27.01604394 14.36132586 12.96693057\n",
      " 17.38712541 24.26517003  9.33669966 17.23427707 30.56321128 27.91590705\n",
      " 14.67488739 20.09351312 27.55410893 24.90721662 23.23640192 11.88592231\n",
      " 31.36779034 28.72921996 17.72000022 28.28589793 15.50082105 15.39465006\n",
      " 22.71104656 22.52863593 10.7356892  33.37194663 30.57526795 10.41443375\n",
      " 30.58160836 15.59976239 24.47489291  9.2931261  25.7780164  32.25964596\n",
      " 26.68878866 14.82468065 27.83655185 19.35279187 33.1348754  16.63044916\n",
      " 29.43461062 22.64553323 26.9186592  15.30987214 15.16666071 16.34161759\n",
      " 23.1321931  17.86089164 18.1454853  19.96324364 23.45196135 38.81583862\n",
      " 28.55717406 16.05136136 24.7221084  27.25053431 21.4963981  38.36321416\n",
      " 43.10705219 12.93654397 22.41809277 21.81618078 27.19452794 25.65808395\n",
      " 25.85450655 24.38168348 20.95854301 41.35816448 37.19852201 16.61141097\n",
      " 18.85498597 17.58637976 18.27475956 24.32285889 33.3526275  24.46064721\n",
      " 27.65771564 21.65691709 -0.29451494 17.49944428]\n"
     ]
    }
   ],
   "source": [
    "prediction=model.predict(x_test)\n",
    "# accuracy=model.score(prediction,y_test)\n",
    "print(prediction)\n",
    "# print(accuracy)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R**2 value : 0.7199983809587347\n",
      "coefficient :  [-1.17835568e-01  3.95494101e-02  1.52330733e-01  5.03056874e+00\n",
      " -1.12327986e+01  4.93261907e+00 -3.32059958e-02 -1.10056362e+00\n",
      "  1.91405446e-01 -9.87512104e-03 -9.60683977e-01  5.97619609e-03\n",
      " -4.90540526e-01]\n",
      "intercept :  26.15022825656005\n"
     ]
    }
   ],
   "source": [
    "print(\"R**2 value :\",linear_reg.score(X,y))\n",
    "print(\"coefficient : \" ,linear_reg.coef_)\n",
    "print(\"intercept : \", linear_reg.intercept_)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test.T[5],y_test)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test.T[5],prediction)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}